a Monto Carlo Simulation to determine the needed time of motion for an MRS supervisor in minesweepers competition see: landminefree.org

MRSMontoCarloSim.py

import random 
import numpy as np
import math
from pprint import pprint as p
LinearSpeed=0.5#linear speed of the Supervisor in m/s
class Arena:
    def __init__(self):
        self.mine_locs=np.zeros(shape=(20,20))
        self.d_loc=[0,0] # x,y ,right hand vector system z is towards you x to the left y to the top of the screen
        self.s_loc=[1,0]
        self.dSteps=0
        self.markings=0
        self.sSteps=0
        self.last_dist=0
    def place_mines(self,nM):
        for i in range(0,nM):
            self.mine_locs[random.randint(0,19)][random.randint(0,19)]=1
    def traverse(self):
        for i in range(400):
            if self.mine_locs[self.d_loc[0]][self.d_loc[1]]==1:
               # import pdb;pdb.set_trace()
                self.sSteps+=self._dist(self.d_loc,self.s_loc)
                self.s_loc[0],self.s_loc[1]=self.d_loc[0],self.d_loc[1]
                self.markings+=1
            if self.d_loc[1]==19:
                self.d_loc[1]=-1
                self.d_loc[0]=self.d_loc[0]+1
            self.d_loc[1]+=1
            self.dSteps+=1
    def _dist(self,p0,p1):
        return int(math.sqrt((p0[0] - p1[0])**2 + (p0[1] - p1[1])**2))
things=[]
for i in range(0,1000):
    a=Arena()
    a.place_mines(16)
    a.traverse()
    things.append((a.sSteps/LinearSpeed)+a.markings*10)
print(np.average(things)//60)